HYPOTHESES

• H₁: The dimensionality of Spotify’s mood-related audio features can be reduced to fewer clusters so that potential differences (\(r\) ≥ .10) can be observed in the stream counts of these clusters between the pre-pandemic and the pandemic period for each and across all DACH countries.
  
  
• H₂: The mood-related clusters and the remaining audio features can successfully be implemented in a classification task that aims to classify both periods in an interpretable way so that a high overall accuracy can be achieved (ACC ≥ 90%).

DATA RETRIEVAL & CLEANING

• Web-scraping: Daily top 200 chart positions, song titles, their stream counts, and track IDs between March 10^th, 2020, and June 14^th, 2020, and for the same period in 2019 (\(N_{Total} = 115198\)) from Spotify’s website for the DACH countries (Germany, Austria, Switzerland)

  • By using arguments of Hadley Wickham’s R package rvest for scraping websites within a simple function and by using his tidyverse):

  • Audio features were collected by using Spotify’s API and the R package “spotifyr” (Thompson et al., 2021) on the previously retrieved song IDs.

EXPLORATORY DATA ANALYSIS

Figure 1
Comparison of Stream Counts of Daily Top 200 Spotify Charts Before and During the Pandemic Per Country and Across all DACH Countries.

Note. A log-scaling with base 10 was applied to the x-axis for visual purposes, mainly, to avoid a heavy tail of the higher stream counts.

CLUSTER SOLUTION

Table 1
K-means Cluster Solution on Min-Max-Normalized and Rescaled Mood-Related Audio Features.

Note. BSS/TSS ratio = 83.3%. Mean values (Danceability, Engery, Loudness, Valence) greater than or equal to .5 represent, e.g., a higher (emotional) positivity (Valence) potentials according to the Arousal-Valence circumplex model. Hence, if the values in Danceablitiy, Energy, and Loudness are also greater than or equal to .5, these values represent a higher arousal potential.

CLUSTER SOLUTION II

Figure 3
3D Scatterplot with Ellipsoids (1st SD) of the K-Means Cluster Solution Across all DACH Countries.

DIFFERENCES

• Multiple comparisons via Dunn test with Holm correction at an alpha-level of 5%.

Figure 4
Combined Box, Violin, and Scatter Plots of the K-Means Cluster Solution for Each DACH Country and Across all DACH Countries Against Their Median Stream Counts Before and During the Pandemic.

• Overlapping cases are better to separate and extreme values are less prone for misclassifications (cf. logistic regression, see James et al., 2013).
• How?
  • Separation between pandemic and non-pandemic cases based on a curved surface in a high-dimensional space (instead of a flat surface) that maximizes the margin around itself.
  • That is, the margin is a distance around the modeled hyperplane, which intercepts as few training cases as possible but as much as necessary (according to the hyperparameters).
  • The cases that intercept the margin are the so-called support vectors because they support the position of the hyperplane (surface).

Model Training

• 80/20 split ratio between training and test set (both sets contain the same track_ids and countries and contain balanced classes; 50/50 ratio)
  
  
• 5-fold cross-validation with random grid search (due to computational costs) to identify the optimal values of the hyperparameters (C and \(\gamma\)), which, simply put, determine to what extent some misclassfications are allowed (avoiding over-/underfitting).
  
  
• Building on the outcomes of this 5-fold-cv, we trained the binary SVM classifier with a radial kernel function (C = 100 and \(\gamma\) = 2), and applied it on the (not seen) test set to classify the two periods in question.

Confusion Matrix

• We assessed the model performance with the accuracy as the key error metric according to which our model could correctly classify observations that indeed belong to the one or the other period (i.e., true positives).

Table 5
SVM Classification: Confusion Matrix

Variable Importance

Figure 5
Permutation-based Variable Importance (Independent Variables) for the SVM Model (Training Set).

Partial dependence

Figure 6
Partial Dependence Plot of the Mood Clusters With Averaged Probabilities Regarding the Classification of the Pandemic Period.

• Songs that belong to the cluster Higher Arousal-Potential pos Emotionality minor and Moderate Arousal-Potential neg Emotionality minor were streamed less during the pandemic in Switzerland.

• In Germany, songs that belong to the cluster Moderate Arousal-Potential neg Emotionality minor were streamed more during the pandemic.

• In Austria, no significant differences could be observed.

• Across all DACH countries, the songs belonging to the clusters Moderate Arousal-Potential neg Emotionality (both in major and minor) have been streamed more often during the pandemic.

• We have evidence to support H₂ based on the outcome of our built binary SVM classifier that yielded a high overall accuracy (ACC = 97.87%, 95% CI [0.977, 0.981]).

• Furthermore, we now know that the mood clusters are the most important input variables in classifying the pandemic period.

• This means, each period shows a distinct profile in terms of the mood-clusters, the used audio features of the track IDs and the grouping factor of the DACH countries.

• All in all, using the top songs helped us to describe the music listening behavior of a great fraction of listeners in central European German-speaking countries via proxy variables.
  
  
• Future studies may want to refer survey data of music listening behavior on those mood clusters with which this data driven approach can be implemented in a more broad study design to gain a more nuanced picture of current everyday music listening.